Medical QA

QA: Question Answering

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Latest papers 44

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Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering
Apr 30, 2026Xupeng Chen, Binbin Shi, Chenqian Le +5Multimodal RAG Retrieval-Augmented Generation
New York University, New York, USA · Tsinghua University, Beijing, China · Independent Researcher +1
Domain Fine-Tuning vs. Retrieval-Augmented Generation for Medical Multiple-Choice Question Answering: A Controlled Comparison at the 4B-Parameter Scale
Apr 26, 2026Avi-ad Avraam BuskilaRetrieval-Augmented Generation Healthcare
Department of Information Science and Applied Artificial Intelligence, Bar-Ilan University, Ramat-Gan, Israel
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
Apr 22, 2026Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1Healthcare Hierarchical Representation Learning
Department of Computer Science, Stony Brook University · Department of Data Science, New Jersey Institute of Technology · Department of Biomedical Informatics, Stony Brook University
Effects of Cross-lingual Evidence in Multilingual Medical Question Answering
Apr 22, 2026Anar Yeginbergen, Maite Oronoz, Rodrigo AgerriKnowledge Augmentation for Language Models Healthcare
HiTZ Center - Ixa, University of the Basque Country UPV/EHU
Beyond Semantic Similarity: A Component-Wise Evaluation Framework for Medical Question Answering Systems with Health Equity Implications
Apr 21, 2026Abu Noman Md Sakib, Md. Main Oddin Chisty, Zijie ZhangLLM Answer Verification Healthcare
University of Texas at San Antonio, USA · Khulna University of Engineering and Technology, Bangladesh
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Apr 19, 2026Alberto Testoni, Iacer CalixtoHealthcare Social Bias in Language Models
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. · Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
Authority Signals in Claude AI Health Citations: A Descriptive Analysis Using the Authority Signals Framework
Apr 17, 2026Erin T. Jacques, Erela Datuowei, Elizabeth Quaye +3Healthcare Medical QA
York College, CUNY · Department of Health and Human Performance, York College, The City University of New York, 94-20 Guy R. Brewer Blvd. Jamaica, NY 11451 · Teachers College, Columbia University +4
Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO
Apr 17, 2026Arash Ahmadi, Parisa Masnadi, Sarah Sharif +3RL for Language Models Healthcare
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA · Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory, University of Oklahoma, Norman, OK, USA · Data Science and Analytics Institute, University of Oklahoma, Norman, OK, USA +3
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
Apr 16, 2026Zhizheng Wang, Chih-Hsuan Wei, Joey Chan +19Deep Research Agents Healthcare
Division of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20892, USA · Hunterian Neurosurgical Laboratory, Johns Hopkins School of Medicine, Baltimore, MD 21231, USA · Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA +7
FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment
Mar 20, 2026Betty Xiong, Jillian Fisher, Benjamin Newman +5Document QA Medical QA
Stanford University · University of Washington · U.S. Food and Drug Administration
RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity
Feb 4, 2026Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2Audio-Language Model Evaluation Audio QA
University of Cambridge · Cambridge, United Kingdom · Tsinghua University +3
Safety boundary maintenance in consumer AI systems responding to pediatric health queries: a cross-platform benchmark evaluation under naturalistic and adversarially pressured conditions
Dec 26, 2025Vahideh Zolfaghari, Leila Mashhadi, Mitra Ahadi +2Healthcare LLM Safety
Medical Sciences Education Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. · Student Research Committee, Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. · Department of Anesthesiology and Intensive Care, Mashhad University of Medical Sciences, Mashhad, Iran. +2
Multilingual Medical Reasoning for Question Answering with Large Language Models
Dec 5, 2025Pietro Ferrazzi, Aitor Soroa, Rodrigo AgerriKnowledge Augmentation for Language Models Healthcare
Fondazione Bruno Kessler, Trento, Italy · HiTZ Center - Ixa, University of the Basque Country EHU, Spain
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Mar 7, 2024Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5Healthcare Medical Diagnosis
2Indian Institute of Technology Bombay · 1IBM Research

Apr 30, 2026, cs.AIJ/K move · Enter open · S save

Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering

Xupeng Chen, Binbin Shi, Chenqian Le, Jiaqi Zhang+4

New York University, New York, USA · Tsinghua University, Beijing, China · Independent Researcher+1

Multimodal RAG Retrieval-Augmented Generation Healthcare Medical VLMs Medical QA Visual Document Retrieval

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Medical retrieval-augmented generation (RAG) systems typically operate on text chunks extracted from biomedical literature, discarding the rich visual content (tables, figures, structured layouts) of original document pages. We propose MED-VRAG, an iterative multimodal RAG framework that retrieves and reasons over PMC document page images instead of OCR'd text. The system pairs ColQwen2.5 patch-level page embeddings with a sharded MapReduce LLM filter, scaling to ~350K pages while keeping Stage-1 retrieval under 30 ms via an offline coarse-to-fine index (C=8 centroids per page, ANN over centroids, exact two-way scoring on the top-R shortlist). A vision-language model (VLM) then iteratively refines its query and accumulates evidence in a memory bank across up to 3 reasoning rounds, with a single iteration costing ~15.9 s and the full three-round pipeline ~47.8 s on 4xA100. Across four medical QA benchmarks (MedQA, MedMCQA, PubMedQA, MMLU-Med), MEDVRAG reaches 78.6% average accuracy. Under controlled comparison with the same Qwen2.5-VL-32B backbone, retrieval contributes a +5.8 point gain over the no-retrieval baseline; we also note a +1.8 point edge over MedRAG + GPT-4 (76.8%), with the caveat that this is a cross-paper rather than head-to-head comparison. Ablations isolate +1.0 from page-image vs text-chunk retrieval, +1.5 from iteration, and +1.0 from the memory bank.

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